metadata_urlCheap metadata-only extraction, no full-page content. Costs $0.002 USDC on Solana via x402, per call — same price as GET /api/v1/metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The public HTTP/HTTPS URL to fetch metadata for |
| Name | Required | Description | Default |
|---|---|---|---|
| og | Yes | ||
| url | Yes | ||
| title | Yes | ||
| favicon | Yes | ||
| description | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the transparency burden. It discloses the cost per call and the scope limitation (no full-page content), which is useful. Yet it omits other behaviors such as error handling, authentication requirements, or rate limits, which are relevant for a paid API call.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences, front-loaded with the key selling points (cost, metadata-only scope), and contains no filler. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (one parameter, no nested objects) and has an output schema, which covers return structure. The description covers purpose, cost, and scope, leaving only minor gaps such as explicit usage exclusions or error behavior, which are not critical for this low-complexity tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides full coverage (100%) of the single `url` parameter with a clear description. The tool description adds no additional parameter-specific semantics beyond what the schema already states, so the default baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The title explicitly lists the extracted data (title, description, Open Graph tags, favicon), and the description adds 'metadata-only extraction' and 'no full-page content,' clearly distinguishing it from the full-page scraping sibling. This is a specific verb+resource+scope definition.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for cheap metadata-only needs via 'Cheap metadata-only extraction' and highlights the cost, which helps decide between this and more expensive full-page tools. However, it does not explicitly name alternative tools or state when not to use it, so it stops short of full guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct extraction goal: specific fields, links, metadata, or full-page markdown. No overlap in purpose, so an agent can confidently select the right tool.
Naming mixes verb_noun (extract_fields, scrape_url) and noun_url (links_url, metadata_url) patterns. The _url suffix on three tools gives some cohesion, but the inconsistency is noticeable.
Four tools is a compact, focused set for a scraping server. It covers the essential extraction tasks without unnecessary bloat, though a few more specialized tools could be added.
The toolkit covers the core scraping workflow: full content, metadata, links, and targeted field extraction. Missing features like raw HTML or DOM handling are minor gaps, but most typical use cases are addressed.